Model-based reinforcement learning algorithms create a representation of the environments to train an agent, rather than only interact with the original environment. This also leads to a significantly more sample efficient solutions than model-free approach [1]}, [2]}. Recently, some researchers have used [3]}, [4]} simple learned world models to assign intrinsic rewards for an agent. However, to our knowledge, no research exist, that bind efficient exploration methods with model-based algorithms.
